8 citations · 23 across the 11 of their papers we have counts for
11 papers
Federated Multi-Task Learning on Non-IID Data Silos: An Experimental Study
Yuwen Yang, Yuxiang Lu, Suizhi Huang +3
The innovative Federated Multi-Task Learning (FMTL) approach consolidates the benefits of Federated Learning (FL) and Multi-Task Learning (MTL), enabling collaborative model traini…
FedHCA: Towards Hetero-Client Federated Multi-Task Learning
Yuxiang Lu, Suizhi Huang, Yuwen Yang +3
Federated Learning (FL) enables joint training across distributed clients using their local data privately. Federated Multi-Task Learning (FMTL) builds on FL to handle multiple tas…
UNIDEAL: Curriculum Knowledge Distillation Federated Learning
Yuwen Yang, Chang Liu, Xun Cai +3
Federated Learning (FL) has emerged as a promising approach to enable collaborative learning among multiple clients while preserving data privacy. However, cross-domain FL tasks, w…
Prompt Guided Transformer for Multi-Task Dense Prediction
Yuxiang Lu, Shalayiding Sirejiding, Yue Ding +2
Task-conditional architecture offers advantage in parameter efficiency but falls short in performance compared to state-of-the-art multi-decoder methods. How to trade off performan…
BPJDet: Extended Object Representation for Generic Body-Part Joint Detection
Huayi Zhou, Fei Jiang, Jiaxin Si +2
Detection of human body and its parts has been intensively studied. However, most of CNNs-based detectors are trained independently, making it difficult to associate detected parts…
Position-Aware Subgraph Neural Networks with Data-Efficient Learning
Chang Liu, Yuwen Yang, Zhe Xie +2
Data-efficient learning on graphs (GEL) is essential in real-world applications. Existing GEL methods focus on learning useful representations for nodes, edges, or entire graphs wi…
EDEN: A Plug-in Equivariant Distance Encoding to Beyond the 1-WL Test
Chang Liu, Yuwen Yang, Yue Ding +1
The message-passing scheme is the core of graph representation learning. While most existing message-passing graph neural networks (MPNNs) are permutation-invariant in graph-level…
Completely Heterogeneous Federated Learning
Chang Liu, Yuwen Yang, Xun Cai +2
Federated learning (FL) faces three major difficulties: cross-domain, heterogeneous models, and non-i.i.d. labels scenarios. Existing FL methods fail to handle the above three cons…
NoMorelization: Building Normalizer-Free Models from a Sample's Perspective
Chang Liu, Yuwen Yang, Yue Ding +1
The normalizing layer has become one of the basic configurations of deep learning models, but it still suffers from computational inefficiency, interpretability difficulties, and l…
On Understanding and Mitigating the Dimensional Collapse of Graph Contrastive Learning: a Non-Maximum Removal Approach
Jiawei Sun, Ruoxin Chen, Jie Li +3
Graph Contrastive Learning (GCL) has shown promising performance in graph representation learning (GRL) without the supervision of manual annotations. GCL can generate graph-level…
On Isotropy Calibration of Transformers
Yue Ding, Karolis Martinkus, Damian Pascual +2
Different studies of the embedding space of transformer models suggest that the distribution of contextual representations is highly anisotropic - the embeddings are distributed in…